1. Field of the Invention
The present invention relates to a learning method of a neural network circuit.
2. Description of the Related Art
At present, computers have been developed remarkably and utilized in various daily scenes. However, so far, development of processing abilities of the computers have been achieved by miniaturization of components (parts) and progresses of algorithms, and basic principles of information processing have not changed. Meanwhile, because of characteristics of processing methods, the computers have a drawback that they are very weak in operations which can be performed easily by humans. For example, the computers are weak in real-time face recognition, understanding of space structures, etc. Processing speeds of these operations of the computers are much lower than those of humans, even if latest algorithms and maximum-scale computers are used.
Under the circumstances, computers which simulate information processing methods of brains of living bodies have been studied. A basic processing model of these processing methods is a neural network.
The neural network is a simulation of a neuron network of a living body. It is known that nerve cells of the living body communicate (give and take) pulses (spike pulses) of a substantially fixed shape. As a neural network circuit which realizes the neural network, there has been proposed a model which truly simulates a neural circuit of a living body and directly handles the pulses. For example, Japanese Laid-Open Patent Application Publication No. Hei. 7-114524 (hereinafter will be referred to as literature 1) discloses a model (pulse density model) representing information using pulse density. This model is an example of a model which represents analog information using the number of pulses propagating for a specified time. Also, Japanese Laid-Open Patent Application Publication No. 2010-146514 (hereinafter will be referred to as literature 2) discloses a model (pulse timing model) which represents information using pulse timings. This model represents analog information using pulses and time intervals of the pulses. These models which use pulse signals have an advantage that hardware can be easily implemented because signals communicated between neurons have a fixed waveform. However, in the pulse density model disclosed in literature 1, it is necessary to extract the pulse density, which requires a certain time to represent the information. Therefore, this pulse density model has a drawback that it cannot represent behaviors of neurons in a minute time scale (time factor). By comparison, the pulse timing model disclosed in literature 2 is able to represent the information using every individual pulse, and therefore execute information processing at a higher speed than the pulse density model.
For example, “W. Maass, “Networks of Spiking Neurons: The Third Generation of Neural Network Models”, Neural Networks, vol. 10, no. 9, pp. 1659-1671, 1997”. (hereinafter will be referred to as literature 3) discloses that higher performance is attained by using the pulse timing model than by using the pulse density model.